Our proposed research addresses the global "systems level" behavior of cells, especially dynamic localization and expression changes of proteins and RNA occurring in single cells as the cells differentiate, as well as the genes controlling these processes. We focus on a defined model system in which yeast change morphology in response to mating pheromone, and we intend to map the pheromone-induced spatial reorganization of the majority of yeast proteins and RNAs. To construct this map and define genes affecting the reorganization, we propose a set of experiments based around "cell chips", a newmicroarray-based platform for automated, high-throughput, high-resolution microscopic imaging of cells. Cell chips offer an approach for characterizing yeast cells by measuring, comprehensively and at the single cell level, protein and RNA expression and localization changes. On each chip, localization and expression can be measured either for a given target protein/RNA across a comprehensive set of genetic backgrounds or for a comprehensive set of proteins/RNAs in a single genetic background. Because >100 cell chips can be made per session from a single strain collection and each chip can be probed with fluorescent probes for a different target, cell chips offer new types of high-throughput genetic interaction screens effectively inaccessible by conventional approaches. We propose to develop cell chip technology to measure single-cell level protein/RNA function, expression and localization on a large scale, focusing on global changes accompanying the response to mating pheromone. Beyond describing the spatial dynamics of the proteome/transcriptome, this work will address the extent to which RNAs are localized in yeast, as well as to compare transcription versus translation levels in single cells for a large fraction of the yeast proteome. These experiments will inform us about general principles of eukaryotic cell differentiation, RNA localization, and post-transcriptional regulation. The experimental and computational technologies we propose should readily extend to other systems, including human cells, moving us closer to "systems level" descriptions of eukaryotic cell behavior. The model system we study has important elements conserved in human cells, and therefore results of this study directly impact our understanding of human cell biology and signal transduction, and the interaction of human cells with the environment.